Statistical shape and texture model of quadrature phase information for prostate segmentation

International audiencePurpose: Prostate volume estimation from segmentation of transrectal ultrasound (TRUS) images aids in diagnosis and treatment of prostate hypertro- phy and cancer. Computer-aided accurate and compu- tationally efficient prostate segmentation in TRUS im- ages is a challenging task, owing to low signal-to-noise ratio, speckle noise, calcifications and heterogeneous in- tensity distribution in the prostate region. Method: A multi-resolution framework using texture features in a parametric deformable statistical model of shape and appearance was developed to segment the prostate. Local phase information of log-Gabor quadra- ture filter extracted texture of the prostate region in TRUS images. Large bandwidth of log-Gabor filter en- sures easy estimation of local orientations and zero re- sponse for a constant signal provides invariance to gray level shift. This aids in enhanced representation of the underlying texture information of the prostate unaf- fected by speckle noise and imaging artifacts. The para- metric model of the propagating contour is derived from principal component analysis of prior shape and texture information of the prostate from the training data. The Soumya Ghose*, Jhimli Mitra*, Arnau Oliver, Robert Mart'ı, Xavier Llad'o and Jordi Freixenet Computer Vision and Robotics Group, University of Girona Campus Montilivi, Edifici P-IV,17071 Girona, Spain. E-mail: [email protected], [email protected], {aoliver, marly, llado, and jordif}@eia.udg.edu Joan C.Vilanova Clinica Girona, Calle Joan Maragall 26, 17002 Girona, Spain. Josep Comet University Hospital Dr. Josep Trueta, Av. Frana, 17007 Girona, Spain. Fabrice Meriaudeau *Laboratoire Le2I - UMR CNRS 5158, Universit'e de Bour- gogne,12 Rue de la Fonderie, 71200 Le Creusot, Bourgogne, France. E-mail: [email protected]. parameters were modified using prior knowledge of the optimization space to achieve segmentation. Results: The proposed method achieves a mean Dice similarity coefficient value of 0.95±0.02, and mean ab- solute distance of 1.26±0.51 millimeter when validated with 24 TRUS images of 6 datasets in a leave-one- patient-out validation framework. Conclusions: The proposed method for prostate TRUS image segmentation is computationally efficient and pro- vides accurate prostate segmentations in presence of in- tensity heterogeneities and imaging artifacts